Generative AI is Here: What Next for Contact Centers?

Generative AI

Automation is leading us to a world that was once only imagined as possible in science fiction, with its transformative effects being felt virtually across every industry. The arrival of Generative AI apps like ChatGPT, Dall-E, etc., has got millions mesmerized and speculating about the onslaught of automation and its potential impact on the future of work. Contact Center is one such industry where emerging agile technologies are transforming the customer service landscape for the better.

According to the World Economic Forum, “The next 10 to 20 years is going to be the golden age of AI.”

What’s significant about this coming ‘golden decade’ is the raised bar of customer expectations. While contact centers have been using Conversational AI for some time, Generative AI can further pave the way for better and more personalized customer experience.

What is Generative AI?

  • It is an AI model capable of creating a wide variety of content (text, music, images, etc.) similar to what a human could make.
  • While most traditional AI models, such as conversational AI, are rules-based and require specific input to produce a particular output, Generative AI can produce results that are not explicitly programmed into it.
  • By analyzing large datasets, it memorizes the underlying data pattern and uses it to create new content. For example: train it on Shakespeare’s sonnets, and it could create a new sonnet similar in style and tone to Shakespearean work.
Also Read: How ChatGPT has Redefined the Role of Conversational AI in Contact Centers?

Supercharging Contact Centers with Generative AI

Integrating Generative AI into contact center operations could pave the way for the transformation of the traditional contact center experience. It could prove instrumental in taking customer service experience to a whole new level by automating routine tasks and empowering agents to provide personalized and proactive customer service by analyzing customer data and identifying patterns, preferences, and pain points, allowing companies to anticipate customers’ needs and deliver tailored solutions even before they ask for them.

1. Deliver Immersive & Phygital Customer Experiences

As per NRF 2023, “A majority of businesses agree that digital and in-store experiences will increasingly merge over the next few years, but only 22% of leaders have the tools to create a seamless experience between the online and physical spaces.”

Phygital Customer Experiences

Generative AI can effectively bridge the gap between the physical and digital worlds. How?
Let’s consider a hypothetical scenario:

A customer contacts a company’s contact center to inquire about a product. The Generative-AI-powered chatbot greets the customer and uses NLP to understand the customer’s message and accordingly offers relevant information about the product, including its features, pricing, and availability. The chatbot can then offer to show a demo of the product using Augmented Reality (AR) technology. The customer could view the product in 3D and interact with it in a virtual environment, getting a better understanding of its features and functionality.

 In addition, Generative AI technology could facilitate a seamless handover between the chatbot and human agent in case customer inquiries require more complex and personalized attention. 

2. Consistent and Seamless Experience Across Channels

“Customers are 2.4X more likely to stay with companies that resolve their queries quickly” – Forrester

Hyper-personalization is not just another passing trend but rather a necessity for contact centers to survive and thrive in today’s competitive scenario. One aspect of this personalized service requires catering to customers on their preferred channel of choice and allowing them to switch channels without letting go of the context. Curating an omnichannel experience allows your business to give customers faster resolution times, thereby ensuring their satisfaction and retention rates.

Now, with Generative AI in the picture, the next logical extension of the omnichannel customer support journey would be in the multiverse, wherein virtual agents can aid in the resolution of customer queries and redefine the customer experience. What’s significant about this journey is that apart from serving customers across multiple mediums, it adds to their experience by allowing them to interact with support teams in an immersive manner with something as simple as a VR headset or holograms.

Generative AI-powered virtual agents can display empathy and accurately assist with customer queries, by enabling them to speak the language of your customer, no matter where they are in the world or what language they speak. As part of an omnichannel solution, Generative AI helps in translating the context to any alternate dialect needed by live agents across CRM platforms in real-time.

Also Read: 5 Ways AI-powered Omnichannel Contact Centers can Deliver Better Customer Experience

3. Next-gen Self-service Solutions

Although we are familiar with the impact of AI on self-service, Generative AI is all set to take it to the next level of efficiency and personalization. From training AI across case-notes written by agents to automatically generating drafts of knowledge articles, it can significantly cut down the time to create knowledge and make it easier to keep articles up to date.

Deliver Immersive & Phygital Customer Experiences

Layering generative AI on top of existing contact center capabilities can automate the creation of smarter chatbots that offer more personalized chat responses and can deeply understand, anticipate, and respond to customer issues. With Generative AI tapping into customer resolution data to analyze customer sentiment and patterns, contact centers can drive continuous improvements, identify latest trends and accelerate bot training. These sort of interaction scenarios allow customers to self-solve minor and repetitive issues such as password requests, amendments, cancellations, etc. with the assurance that live agents are ready and available to tackle complex queries.

A smart, Generative AI-enabled FAQ design can further help elaborate upon existing answers by providing links to relevant blogs, in-depth articles, product pages, and alternative resources like a customer support phone number or a live-messaging service. Similarly, by using analytics driven by Machine Learning (ML) technology, Generative AI could pave the way for continuous improvements and enable it to deliver solutions that are more responsive and relatable to customers.

Will Generative AI Replace Humans?

67% of customers believe that generative AI will soon play a crucial role in customer service – Zendesk

The Zendesk report also highlighted that three in four customers who have used generative AI are comfortable with agents using it to help their questions. But this is not to say that they are ready to let go of the accuracy and empathy that human agents bring in. There have been instances of customers being frustrated by bots that provide inaccurate responses and while generative AI can offer more relevant information to customers there will always be questions that would require the problem-solving skills and empathy of a human agent.

So, while generative AI can assist humans by identifying patterns, trends and sentiments, enabling contact centers to make data-driven decisions, it is unlikely to replace human agents completely. Rather, the combination of generative AI and human expertise can provide a superior customer experience and drive better business outcomes.

Wrap Up

The future of Generative AI in the contact center industry is promising, with the potential to revolutionize the way customer support is provided. However, it is crucial to approach the implementation of these technologies carefully and with a focus on ethical considerations to ensure that they are utilized in a responsible and effective manner. By training the Generative AI models on diverse data sets and balancing the use of AI with a human touch, contact centers can overcome the many setbacks that Generative AI currently faces.

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